
import torch

from network.backbone.Crossfomer import CrossFormer

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = CrossFormer(img_size=224,
        patch_size=[4, 8, 16, 32],
        in_chans= 3,
        num_classes=1000,
        embed_dim=48,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        group_size=[7, 7, 7, 7],
        mlp_ratio=4.,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        drop_path_rate=0.1,
        ape=False,
        patch_norm=True,
        use_checkpoint=False,
        merge_size=[[2, 4], [2,4], [2, 4]]
    )
    output=model(input)
    print(output.shape)